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I usually describe myself as half a sociologist and half an applied statistician, although for most of my career the sociologist predominated with a broad interest in social stratification and inequality. This duality dates back to my undergraduate days at Oberlin College where I was a double major in sociology and math but took more hours in sociology than I could count towards my degree. My substantive research primarily focuses on economic structure and labor market inequality, especially with respect to race-ethnicity and gender.

For example, I study how job segregation and devaluation processes create and reproduce race and gender inequalities in job rewards. But I have also dabbled over the years in other realms of race-ethnic inequality, including research on wealth, home equity, residential segregation, traffic stops and treatment by police, and most recently on media portrayals of crime. In terms of my interests in applied statistics and quantitative methodology, my research has usually been explicitly tied to particular substantive questions such as how to estimate "tolerable" segregation, the use of cluster analysis to define economic segments, or the use of multiplicity sampling of workers to create a representative sample of work organizations. But some--like my recent work on detecting and correcting for heteroskedasticity or my current work on interpreting interaction effects in generalized linear models--is motivated by more abstract statistical issues.